Molecular Biomarkers and Machine Learning in Oral Cancer: A Systematic Review and Meta‐Analysis

ABSTRACT Objective This systematic review and meta‐analysis aimed to synthesize diagnostic and prognostic performance metrics of machine learning (ML)‐based biomarker models in oral squamous cell carcinoma (OSCC) and to integrate biological insights through a functional metasynthesis. Methods Following PRISMA 2020 guidelines, a comprehensive search was conducted up to July 2025. Eligible studies applied ML algorithms to molecular or imaging biomarkers from OSCC patients. Data synthesis incorporated meta‐analysis when endpoints and designs were sufficiently comparable; otherwise, study‐level results were summarized narratively. Results Twenty‐five studies encompassing 4408 patients were included. Diagnostic performance was strongest for salivary DNA methylation (AUC up to 1.00), metabolomics (AUC ≈0.92), and FTIR imaging (AUC ≈0.91), while autoantibody and microbiome models showed more variable accuracy. Prognostic models based on immune‐feature signatures outperformed conventional scores, while multimodal approaches integrating imaging and metabolomics retained strong performance under external validation. Models based on pathomics and MRI radiomics also achieved clinically meaningful accuracy across independent cohorts. Functional metasynthesis revealed convergent biological processes—metabolic reprogramming, immune–inflammatory remodeling, microbiome dysbiosis, and epithelial/extracellular matrix disruption—that underpin predictive accuracy. Conclusion ML models leveraging molecular and imaging biomarkers show strong potential to improve OSCC diagnosis, risk stratification, and prognosis, particularly through multimodal integration.